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1. Algorithmic Trading: What Is It and How Does It Work? Algorithmic trading is a type of trading that uses complex algorithms to determine the best time to buy or sell a stock or other financial instrument. Algorithmic trading systems use mathematical models and computer programs to make decisions about when to buy and sell. These systems can scan markets for potential opportunities, monitor news and events, and execute trades according to predetermined rules. Algorithmic trading has become increasingly popular as technology and computing power have advanced, making it possible to execute trades faster and with greater precision. Algorithmic trading can be used to help traders achieve better returns and reduce risk by eliminating emotions from the decision-making process.
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PilotANN: A Hybrid CPU-GPU System For Graph-based ANNS - MarkTechPost
Approximate Nearest Neighbor Search (ANNS) is a fundamental vector search technique that efficiently identifies similar items in high-dimensional vector spaces. Traditionally, ANNS has served as the backbone for retrieval engines and recommendation systems, however, it struggles to keep pace with modern Transformer architectures that employ higher-dimensional embeddings and larger datasets. Unlike deep learning systems that can be horizontally scaled due to their stateless nature, ANNS remains centralized, creating a severe single-machine throughput bottleneck. Empirical testing with 100-million scale datasets reveals that even state-of-the-art CPU implementations of the Hierarchical Navigable Small World (HNSW) algorithm can’t maintain adequate performance as vector dimensions
Are data scientists obsolete in the agentic era? - DataScienceCentral.com
How AI agents are reshaping one of the most prestigious tech roles of the past decade In a corner office of a Fortune 500 company, a team of data scientists once spent weeks crafting algorithms to predict customer churn. Today, a business user without data science support can craft an AI agent to perform same… Read More »Are data scientists obsolete in the agentic era?
Researchers teach LLMs to solve complex planning challenges | MIT News | Massachusetts Institute of Technology
A new large language model framework teaches LLMs to use an optimization solving algorithm to resolve complex, multistep planning tasks. With the LLMFP framework, someone can input a natural language description of their problem and receive a plan to reach their desired goal.
This AI Paper from ByteDance Introduces a Hybrid Reward System Combining Reasoning Task Verifiers (RTV) and a Generative Reward Model (GenRM) to Mitigate Reward Hacking - MarkTechPost
Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning LLMs with human values and preferences. Despite introducing non-RL alternatives like DPO, industry-leading models such as ChatGPT/GPT-4, Claude, and Gemini continue to rely on RL algorithms like PPO for policy optimization. Recent research focuses on algorithmic improvements, including eliminating critic models to reduce computational costs, filtering noisy samples during PPO sampling, and enhancing reward models to mitigate reward hacking problems. However, only a few studies focus on RLHF data construction (i.e., training prompts) and its performance scaling based on these training prompts. The success of RLHF heavily depends on reward